The 4^th Dimension of Movement

Manuscript Figures (simulated tracks)

Preparation

## packages
library(readr)
library(dplyr)
library(forcats)
library(purrr)
library(raster)
library(ggplot2)
library(ggh4x)
library(ggtext)
library(scico)
library(patchwork)
library(here)

## plot theme
theme_set(d6::theme_d6(base_size = 18, legend = "bottom"))

theme_update(
  panel.grid.major.x = element_blank(),
  panel.grid.major.y = element_line(color = "#fefefe", linewidth = .5),
  panel.grid.minor = element_blank(),
  panel.background = element_rect(fill = "grey95", color = "grey95"),
  panel.border = element_rect(fill = "transparent", color = "transparent"),
  panel.spacing.x = unit(.9, "lines"),
  panel.spacing.y = unit(1.5, "lines"),
  legend.justification = "left",
  legend.box.margin = margin(-20, 0, 0, 0),
  legend.text = element_text(family = "PT Sans Narrow"),
  axis.text = element_text(family = "PT Mono", size = rel(.7)),
  axis.text.x = element_text(margin = margin(t = 5)),
  axis.text.y = element_text(margin = margin(r = 5)),
  axis.text.y.right = element_text(hjust = 1),
  axis.ticks.length = unit(0, "lines"),
  strip.text = element_text(color = "black"),
  strip.text.x = element_markdown(face = "plain", vjust = 0, lineheight = 1.05),
  strip.text.y.left = element_markdown(angle = 0, hjust = 0, vjust = .5),
  strip.background = element_rect(color = "transparent", fill = "transparent"),
  strip.placement = "outside",
  plot.title = element_markdown(),
  plot.subtitle = element_text(hjust = .5, size = rel(1.12)),
  plot.margin = margin(rep(1.5, 4))
)

## path + folder for plots
dir_plots <- here("plots")
if (!dir.exists(dir_plots)) dir.create(dir_plots, recursive = TRUE)

Import Simulated Landscapes

lscp_elv <- read_rds(here("data", "simulated", "landscapes", "1", "cont.rds"))
lscp_hab <- read_rds(here("data", "simulated", "landscapes", "1", "cat.rds"))
lscp_hr <- read_rds(here("data", "simulated", "landscapes", "hr.rds"))

lscp_mix <- 
  list(
    calc(stack(lscp_elv[[1]], lscp_hab[[1]]), mean),
    calc(stack(lscp_elv[[2]], lscp_hab[[2]]), mean),
    calc(stack(lscp_elv[[3]], lscp_hab[[3]]), mean),
    calc(stack(lscp_elv[[3]], lscp_hab[[1]]), mean),
    calc(stack(lscp_elv[[1]], lscp_hab[[3]]), mean)
  )

Import Simulation Results

dat_est <- read_rds(here("output", "simulated", "results_combined.rds"))

Plot Settings

colors <- c(`Elevation (numeric)` = "#CD9804", `Habitat (categorical)` = "#1B9E77")
width <- 1.2
spacing <- .6

Visualizations

Figure 1

Raw maps for conceptual figure

plot_map <- function(raster, layer, titles = c("low", "intermediate", "high")) {
  
  dat <- 
    raster[[layer]] |>  
    raster::rasterToPoints() |>
    as_tibble()

  if (names(dat)[3] == "var1") var <- "elevation"
  if (names(dat)[3] == "var2") var <- "habitat"
  if (names(dat)[3] == "layer") var <- "combined"
  if (names(dat)[3] == "d") var <- "directionality"
  
  if (var == "habitat") dat <- mutate(dat, var2 = factor(var2, levels = c(1, 0), labels = c("Habitat", "Matrix")))
  
  title <- titles[layer]
  
  # if (layer == 1) { title <- "low" }
  # if (layer == 2) { title <- "intermediate" }
  # if (layer == 3) { title <- "high" }
  
  map <-
    dat |> 
    dplyr::select("x" = 1, "y" = 2, "z" = 3) |> 
    ggplot(aes(x, y, fill = z)) + 
    geom_raster() +
    scale_x_continuous(name = NULL, labels = scales::label_comma()) +
    scale_y_continuous(name = NULL, labels = scales::label_comma()) +
    coord_equal(clip = "off", expand = FALSE) +
    labs(subtitle = title) +
    theme(
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank(),
      panel.background = element_blank(),
      panel.border = element_blank(),
      legend.position = "bottom",
      legend.direction = "vertical",
      plot.subtitle = element_text(margin = margin(15, 1, -5, 1)),
      axis.text.x = element_blank(),
      axis.text.y = element_blank(),
      #axis.text = element_text(margin = margin(t = 12), family = "PT Mono"),
      #axis.text.x = element_text(margin = margin(t = 12)),
      #axis.text.y = element_text(margin = margin(r = 12)),
      axis.ticks.length = unit(0, "lines"),
      plot.margin = margin(rep(1, 4))
    )
  
  if (var == "elevation") {
    map <- map +
      #scale_fill_scico(palette = "bamako", midpoint = .5, limits = c(0, 1), name = NULL) +
      scale_fill_gradientn(
        colors = c("#feedbf", "#fde298", "#fcd872", "#fbcd4b", "#fbc225", "#f3b505", "#cd9804", "#a77b03", "#805f02", "#5a4202"),
        name = NULL
      ) +
      theme(
        legend.text = element_text(family = "PT Mono"),
        legend.key.width = unit(7.5, "lines"), legend.key.height = unit(.75, "lines"),
        legend.direction = "horizontal", legend.ticks = element_blank()
      )
  }
  
  if (var == "habitat") {
    map <- map +
      scale_fill_manual(values = c("#00664A", "#E0DDC9"), name = NULL) +
      theme(legend.text = element_text(family = "PT Sans Narrow", size = rel(1)),
            legend.key.spacing.y = unit(.2, "lines"))
  }
  
  if (var == "combined") {
    map <- map +
      scale_fill_gradientn(
        colors = c('#ffea9b', '#e0d689', '#c0c27e', '#a1af72', '#839c67', '#65895a', '#49764d', '#2b6340', '#005133'),
        limits = c(0, 1), name = NULL
      ) +
      theme(
        legend.text = element_text(family = "PT Mono"), legend.justification = "center",
        legend.key.width = unit(7.5, "lines"), legend.key.height = unit(.75, "lines"),
        legend.direction = "horizontal", legend.ticks = element_blank(),
        plot.subtitle = element_markdown()
      )
  }
  
  if (var == "directionality") {
    
    lims <- range(dat$d)
    
    map <- map +
      scico::scale_fill_scico(
        palette = "lapaz", direction = -1, name = NULL, limits = lims,
        breaks = lims, labels = c("high", "low")
      ) +
      theme(
        legend.text = element_text(family = "PT Sans Narrow"), legend.justification = "center",
        legend.key.width = unit(2.5, "lines"), legend.key.height = unit(.75, "lines"),
        legend.direction = "horizontal", legend.ticks = element_blank(),
        plot.subtitle = element_markdown()
      )
  }
  
  return(map)
}

list_elv <- map(1:3, ~plot_map(lscp_elv, .x))

maps_elv <- 
  list_elv[[1]] + plot_spacer() + list_elv[[2]] + plot_spacer() + list_elv[[3]] +
  plot_layout(guides = "collect", widths = c(1, .1, 1, .1, 1)) +
  plot_annotation(title = "**Elevation** (numeric)", 
                  theme = theme(legend.box.margin = margin(-5, 1, -5, 5),
                                plot.title = element_markdown(face = "plain", hjust = 0),
                                plot.title.position = "plot"))

ggsave(paste0(dir_plots, "/_raw/1_s1_sim_maps_elv_1.png"), width = 8, height = 4.7, dpi = 600)

maps_elv

list_hab <- map(1:3, ~plot_map(lscp_hab, .x))

maps_hab <- 
  list_hab[[1]] + plot_spacer() + list_hab[[2]] + plot_spacer() + list_hab[[3]] +
  plot_layout(guides = "collect", widths = c(1, .1, 1, .1, 1)) +
  plot_annotation(title = "**Habitat** (categorical)", 
                  theme = theme(legend.justification = "left", 
                                legend.box.margin = margin(-9, 1, -5, 1),
                                plot.title = element_markdown(face = "plain", hjust = 0),
                                plot.title.position = "plot"))

ggsave(paste0(dir_plots, "/_raw/1_s1_sim_maps_hab_1.png"), width = 8, height = 4.7, dpi = 600)

maps_hab

list_mix <- map(1:5, ~plot_map(lscp_mix, .x, titles = c("both<br>low", "both<br>intermediate", "both<br>high", 
                                                        "Elevation high<br>Habitat low", "Elevation low<br>Habitat high")))

maps_mix <- 
  list_mix[[1]] + plot_spacer() + list_mix[[2]] + plot_spacer() + list_mix[[3]] + plot_spacer() + list_mix[[4]] + plot_spacer() + list_mix[[5]] +
  plot_layout(guides = "collect", widths = c(1, .1, 1, .1, 1, .1, 1, .1, 1), nrow = 1) +
  plot_annotation(title = "**Combined variables**", 
                  theme = theme(legend.justification = "left", 
                                legend.box.margin = margin(0, 1, -5, 1),
                                plot.title = element_markdown(face = "plain", hjust = 0),
                                plot.title.position = "plot"))

ggsave(paste0(dir_plots, "/_raw/1_sim_maps_mix_1.png"), width = 12, height = 4.7, dpi = 600)

maps_mix

map_hr <- 
  plot_map(lscp_hr, 1, titles = NULL) +
  ggtitle("Homing tendency") +
  theme(
    legend.justification = "left", 
    legend.box.margin = margin(0, 1, -5, 1),
    plot.title = element_markdown(face = "bold", hjust = 0),
    plot.title.position = "plot",
    legend.key.width = unit(3, "lines")
  )

ggsave(paste0(dir_plots, "/_raw/1_sim_maps_dir.png"), width = 4, height = 4.7, dpi = 600)

map_hr

Figure 3

Estimates across methods and levels of autocorrelation per environmental variable without homing tendency (hr)

dat_fig3 <-  
  bind_rows(
    dat_est |> filter(var1 != 0, var2 == 0, hr == 0, lscp_var2_ac == 1) |>
      mutate(lscp_var_ac = lscp_var1_ac),
    dat_est |> filter(var1 == 0, var2 != 0, hr == 0, lscp_var1_ac == 1) |>
      mutate(lscp_var_ac = lscp_var2_ac),
  ) |>
  mutate(
    method = factor(method, labels = c("**iSSA**", "**RSA**", "**wRSA**")),
    term = if_else(term == "var1", "Elevation (numeric)", "Habitat (categorical)"),
    lscp_var_ac = factor(lscp_var_ac, levels = 1:3,
                         labels = c("low", "**Level of autocorrelation**<br>intermediate", "high")))

labs_fig3 <- 
  dat_fig3 |> 
  filter(by == 1, method == "**iSSA**", lscp_var_ac == "low") |> 
  mutate(
    pos = if_else(term == "Elevation (numeric)", max(estimate), min(estimate)),
    lab = if_else(term == "Elevation (numeric)", "Elevation", "Habitat"),
    just = if_else(term == "Elevation (numeric)", -.8, 1.8),
  ) |> 
  dplyr::select(by, method, term, lscp_var_ac, pos, just, lab) |> 
  unique()

ggplot(
    data = dat_fig3 |> filter(estimate > -50), 
    aes(x = factor(by), y = estimate)
  ) +
  geom_violin(
    aes(color = term, fill = after_scale(prismatic::clr_lighten(color, .35))), 
    position = position_dodge(width = spacing), width = width, 
    bw = .7, linewidth = 0
  ) +
  geom_hline(yintercept = 0, color = "#BABABA") +
  geom_violin(
    aes(color = term, fill = after_scale(prismatic::clr_lighten(color, .35))), 
    position = position_dodge(width = spacing), width = width, 
    bw = .7, alpha = .4, linewidth = 0
  ) +
  stat_summary(
    aes(color = term, color = after_scale(prismatic::clr_darken(color, .35))),
    fun.data = "mean_sdl", fun.args = list(mult = 1),
    position = position_dodge(width = spacing),
    size = .2
  ) +
  geom_text(
    data = labs_fig3, 
    aes(y = pos, label = lab, vjust = just,
        color = term),
    position = position_dodge(width = spacing),
    family = "PT Sans Narrow", fontface = "bold", size = 5, show.legend = FALSE
  ) +
  geom_text(
    data = dat_fig3 |> filter(estimate < -50) |> mutate(estimate = -34), 
    aes(color = term, label = "↓ outlier with an estimate of —85.9 not shown"),
    position = position_nudge(x = .13),
    hjust = 0, size = 3.9, family = "PT Sans Narrow", show.legend = FALSE
  ) +
  facet_grid(method ~ lscp_var_ac, scale = "free_y", space = "free_y", switch = "y") +
  scale_y_continuous(position = "right", breaks = -3:3*10) +
  scale_color_manual(values = colors) +
  labs(x = "Sampling interval", y = "Estimate", color = NULL)

ggsave(paste0(dir_plots, "/3_sim_est_lscp_ac.png"), width = 12, height = 7.8, dpi = 600, bg = "white")

Figure 1

Figure 4

Estimates across methods and levels of autocorrelation per environmental variable for simulations with homing tendency (hr)

dat_fig4 <- 
  bind_rows(
    dat_est |> filter(var1 != 0, var2 != 0, hr != 0,
                  lscp_var1_ac == lscp_var2_ac) |>
      mutate(lscp = as.character(lscp_var1_ac)),
    dat_est |> filter(var1 != 0, var2 != 0, hr == 0, lscp_var1_ac == 3, lscp_var2_ac == 1) |>
      mutate(lscp = "var1 ↑; var2 ↓"),
    dat_est |> filter(var1 != 0, var2 != 0, hr == 0, lscp_var1_ac == 1, lscp_var2_ac == 3) |>
      mutate(lscp = "var1 ↓; var2 ↑")
  ) |> 
  mutate(
    term = if_else(term == "var1", "Elevation (numeric)", "Habitat (categorical)"),
    method = factor(method, labels = c("**iSSA**", "**RSA**", "**wRSA**")),
    group = if_else(lscp %in% 1:3, "<b style='font-size:18pt;'>Same autocrorrelation levels for both variables</b>", "<b style='font-size:18pt;'>Different autocorrelation levels</b>"),
    group = factor(group, levels = c("<b style='font-size:18pt;'>Same autocrorrelation levels for both variables</b>", "<b style='font-size:18pt;'>Different autocorrelation levels</b>")),
    lscp = factor(lscp, labels = c("low", "intermediate", "high", "Elevation high<br>Habitat low", "Elevation low<br>Habitat high"))
  )

ggplot(dat_fig4, aes(x = factor(by), y = estimate)) +
  geom_violin(
    aes(color = term, fill = after_scale(prismatic::clr_lighten(color, .35))), 
    position = position_dodge(width = spacing), width = width, 
    bw = .7, linewidth = 0
  ) +
  geom_hline(yintercept = 0, color = "#BABABA") +
  geom_violin(
    aes(color = term, fill = after_scale(prismatic::clr_lighten(color, .35))), 
    position = position_dodge(width = spacing), width = width, 
    bw = .7, alpha = .4, linewidth = 0
  ) +
  stat_summary(
    aes(color = term, color = after_scale(prismatic::clr_darken(color, .35))),
    fun.data = "mean_sdl", fun.args = list(mult = 1),
    position = position_dodge(width = spacing),
    size = .2
  ) +
  facet_nested(
    method ~ group + lscp,
    axes = "margins", remove_labels = "x", 
    scales = "free_y", space = "free_y", switch = "y"
  ) +
  scale_y_continuous(position = "right") + #, breaks = -3:3*10
  scale_color_manual(values = colors) +
  labs(x = "Sampling interval", y = "Estimate", color = NULL) +
  theme(
    strip.text.x = element_markdown(size = rel(.85), vjust = .5, margin = margin(6, 1, 2, 1)),
    ggh4x.facet.nestline = element_line(colour = "#c6c6c6")
  )

ggsave(paste0(dir_plots, "/4_sim_est_lscp_ac_hr.png"), width = 12, height = 8.4, dpi = 600, bg = "white")

Figure 2

dat_fig4_out <- 
  dat_fig4 |> 
  filter(estimate < -50) |> 
  mutate(estimate = -50)

out_lab <- paste(nrow(dat_fig4_out), "estimates lower than -50 ↓")

ggplot(
    data = dat_fig4 |> filter(estimate > -50),
    aes(x = factor(by), y = estimate)
  ) +
  geom_violin(
    aes(color = term, fill = after_scale(prismatic::clr_lighten(color, .35))), 
    position = position_dodge(width = spacing), width = width, 
    bw = .7, linewidth = 0
  ) +
  geom_hline(yintercept = 0, color = "#BABABA") +
  geom_violin(
    aes(color = term, fill = after_scale(prismatic::clr_lighten(color, .35))), 
    position = position_dodge(width = spacing), width = width, 
    bw = .7, alpha = .4, linewidth = 0
  ) +
  stat_summary(
    aes(color = term, color = after_scale(prismatic::clr_darken(color, .35))),
    fun.data = "mean_sdl", fun.args = list(mult = 1),
    position = position_dodge(width = spacing),
    size = .2
  ) +
  geom_text(
    data = dat_fig4_out |> slice(1), 
    aes(color = term, label = out_lab),
    position = position_nudge(x = .23),
    hjust = 1, size = 3.9, family = "PT Sans Narrow", show.legend = FALSE
  ) +
  facet_nested(
    method ~ group + lscp,
    axes = "margins", remove_labels = "x", 
    scales = "free_y", space = "free_y", switch = "y"
  ) +
  scale_y_continuous(position = "right") + #, breaks = -3:3*10
  scale_color_manual(values = colors) +
  labs(x = "Sampling interval", y = "Estimate", color = NULL) +
  theme(
    strip.text.x = element_markdown(size = rel(.85), vjust = .5, margin = margin(6, 1, 2, 1)),
    ggh4x.facet.nestline = element_line(colour = "#c6c6c6")
  )

ggsave(paste0(dir_plots, "/4_sim_est_lscp_ac_hr_zoom.png"), width = 12, height = 8.4, dpi = 600, bg = "white")

Figure 5

Mean estimates + shares of significant outcomes levels of autocorrelation, and directionality per method x environmental variable

dat_labs <-  
  dat_est |> 
  mutate(
    across(lscp_var1_ac:hr, ~ paste0(cur_column(), " = ", .x), .names = "lab_{col}")
  )

# Contin.
dat_fig5 <-
  bind_rows(
    dat_est |>
      # only var 1
      filter(var1 == 2, var2 == 0, lscp_var2_ac == 1, term == "var1",
             hr != 0) |>
      mutate(lscp = as.character(lscp_var1_ac)) |>
      group_by(by, lscp, method, term) |>
      summarise(mean = mean(estimate, na.rm = TRUE),
                p.sig = round(mean(low > 0 & high > 0, na.rm = TRUE), 2),
                n = n()),
  
    # only var 2
    dat_est |>
      filter(var1 == 0, var2 == -2, lscp_var1_ac == 1, term == "var2") |>
      mutate(lscp = as.character(lscp_var2_ac)) |>
      group_by(by, lscp, method, term) |>
      summarise(mean = mean(estimate, na.rm = TRUE),
                p.sig = round(mean(low < 0 & high < 0, na.rm = TRUE), 2),
                n = n()),
  
    # var 1 and var 2 same landscape ac
    dat_est |>
      filter(var1 != 0, var2 != 0, lscp_var1_ac == lscp_var2_ac) |>
      mutate(lscp = paste0("both", lscp_var1_ac)) |>
      group_by(by, lscp, method, term) |>
      summarise(mean = mean(estimate, na.rm = TRUE),
                p.sig = 1 - round(mean(low < 0 & high > 0, na.rm = TRUE), 2),
                n = n()),
    # One high and one low
    dat_est |> filter(var1 != 0, var2 != 0, hr == 0, lscp_var1_ac == 3, lscp_var2_ac == 1) |>
      mutate(lscp = "var1 high; var2 low") |>
      group_by(by, lscp, method, term) |>
      summarise(mean = mean(estimate, na.rm = TRUE),
                p.sig = 1 - round(mean(low < 0 & high > 0, na.rm = TRUE), 2),
                n = n()),
    dat_est |> filter(var1 != 0, var2 != 0, hr == 0, lscp_var1_ac == 1, lscp_var2_ac == 3) |>
      mutate(lscp = "var1 low; var2 high") |>
      group_by(by, lscp, method, term) |>
      summarise(mean = mean(estimate, na.rm = TRUE),
                p.sig = 1 - round(mean(low < 0 & high > 0, na.rm = TRUE), 2),
                n = n())
  ) |> 
  ungroup() |> 
  mutate(
    method = factor(method, labels = c("**iSSA**", "**RSA**", "**wRSA**")),
    term = if_else(term == "var1", "Elevation", "Habitat"),
    lscp = factor(lscp, labels = c("low (single)", "intermediate (single)", "high (single)", 
                                   "low (both)", "intermediate (both)", "high (both)", 
                                   "Elevation high<br>Habitat low", "Elevation low<br>Habitat high")),
    lscp_num = as.numeric(fct_rev(lscp)),
    lscp_num = case_when(lscp_num >= 6 ~ lscp_num + .6, lscp_num < 3 ~ lscp_num - .6, TRUE ~ lscp_num)
  )

ggplot(dat_fig5, aes(x = factor(by), y = lscp_num, fill = mean)) + 
  geom_point(
    aes(size = 100),
    fill = "grey94", shape = 21, color = "transparent"
  ) +
  geom_point(
    data = filter(dat_fig5, p.sig >= .005),
    aes(size = p.sig * 100),
    shape = 21, color = "transparent"
  ) +
  # geom_text(
  #   #aes(label = sprintf("%1.2f", p.sig)), 
  #   aes(label = round(p.sig * 100), color = (mean < -6) | (mean > 5 & p.sig > .05)), 
  #   family = "PT Mono", size = 2.5, fontface = "bold"
  # ) +
  facet_nested(
    ~ method + term,
    axes = "margins", remove_labels = "x"
  ) +
  coord_fixed() +
  scale_y_continuous(expand = c(.07, .07), breaks = unique(dat_fig5$lscp_num), labels = levels(dat_fig5$lscp)) +
  scale_color_manual(values = c("black", "white"), guide = "none") +
  scale_fill_scico(
    palette = "romaO", begin = .15, end = .85,
    midpoint = 0, name = "Mean estimate:", breaks = -4:4*2
  ) +
  scale_size_area(
    max_size = 16, name = "Share of significant outcomes:",
    guide = "none"
  )  +
  labs(x = "Sampling interval", y = NULL) +
  theme(
    legend.title.position = "top", legend.title = element_text(hjust = 0),
    legend.key.width = unit(1, "null"), legend.key.height = unit(.7, "lines"), 
    panel.spacing.x = unit(.5, "lines"),
    panel.background = element_rect(fill = "transparent", color = "transparent"),
    ggh4x.facet.nestline = element_line(colour = "#c6c6c6"),
    axis.text.y = element_markdown(hjust = 0, size = rel(1.1), family = "PT Sans Narrow")
  )

ggsave(paste0(dir_plots, "/_raw/5_sim_est_mean_sign_blank.png"), width = 12, height = 7.5, dpi = 600, bg = "white")

Figure 3

---

Session Info

git2r::repository()

Local:    main /Users/cedric/My Drive/Work/DataViz/Clients/2024_IZW_Support/Signer_TempRes
Remote:   main @ origin (https://github.com/EcoDynIZW/Signer_TempRes.git)
Head:     [44e43f5] 2025-02-24: clean-up plot dirs

devtools::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.2 (2024-10-31)
 os       macOS Ventura 13.6.4
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Europe/Berlin
 date     2025-02-26
 pandoc   3.2 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package     * version    date (UTC) lib source
 backports     1.5.0      2024-05-23 [1] CRAN (R 4.4.0)
 base64enc     0.1-3      2015-07-28 [1] CRAN (R 4.4.0)
 cachem        1.0.8      2023-05-01 [1] CRAN (R 4.4.0)
 checkmate     2.3.1      2023-12-04 [1] CRAN (R 4.4.0)
 class         7.3-22     2023-05-03 [1] CRAN (R 4.4.2)
 classInt      0.4-10     2023-09-05 [1] CRAN (R 4.4.0)
 cli           3.6.3      2024-06-21 [1] CRAN (R 4.4.0)
 cluster       2.1.6      2023-12-01 [1] CRAN (R 4.4.2)
 codetools     0.2-20     2024-03-31 [1] CRAN (R 4.4.2)
 colorspace    2.1-0      2023-01-23 [1] CRAN (R 4.4.0)
 commonmark    1.9.1      2024-01-30 [1] CRAN (R 4.4.0)
 d6            0.1.0.4    2024-06-20 [1] Github (EcoDynIZW/d6@08906ee)
 data.table    1.15.4     2024-03-30 [1] CRAN (R 4.4.0)
 DBI           1.2.3      2024-06-02 [1] CRAN (R 4.4.0)
 devtools      2.4.5      2022-10-11 [1] CRAN (R 4.4.0)
 digest        0.6.35     2024-03-11 [1] CRAN (R 4.4.0)
 dplyr       * 1.1.4      2023-11-17 [1] CRAN (R 4.4.0)
 e1071         1.7-14     2023-12-06 [1] CRAN (R 4.4.0)
 ellipsis      0.3.2      2021-04-29 [1] CRAN (R 4.4.0)
 evaluate      0.23       2023-11-01 [1] CRAN (R 4.4.0)
 fansi         1.0.6      2023-12-08 [1] CRAN (R 4.4.0)
 farver        2.1.1      2022-07-06 [1] CRAN (R 4.4.0)
 fastmap       1.1.1      2023-02-24 [1] CRAN (R 4.4.0)
 forcats     * 1.0.0      2023-01-29 [1] CRAN (R 4.4.0)
 foreign       0.8-87     2024-06-26 [1] CRAN (R 4.4.2)
 Formula       1.2-5      2023-02-24 [1] CRAN (R 4.4.0)
 fs            1.6.4      2024-04-25 [1] CRAN (R 4.4.0)
 generics      0.1.3      2022-07-05 [1] CRAN (R 4.4.0)
 ggh4x       * 0.2.8.9000 2024-10-14 [1] Github (teunbrand/ggh4x@e797c7c)
 ggplot2     * 3.5.1      2024-04-23 [1] CRAN (R 4.4.0)
 ggtext      * 0.1.2      2022-09-16 [1] CRAN (R 4.4.0)
 git2r         0.35.0     2024-10-20 [1] CRAN (R 4.4.1)
 glue          1.8.0      2024-09-30 [1] CRAN (R 4.4.1)
 gridExtra     2.3        2017-09-09 [1] CRAN (R 4.4.0)
 gridtext      0.1.5      2022-09-16 [1] CRAN (R 4.4.0)
 gtable        0.3.5      2024-04-22 [1] CRAN (R 4.4.0)
 here        * 1.0.1      2020-12-13 [1] CRAN (R 4.4.0)
 Hmisc         5.1-3      2024-05-28 [1] CRAN (R 4.4.0)
 hms           1.1.3      2023-03-21 [1] CRAN (R 4.4.0)
 htmlTable     2.4.2      2023-10-29 [1] CRAN (R 4.4.0)
 htmltools     0.5.8.1    2024-04-04 [1] CRAN (R 4.4.0)
 htmlwidgets   1.6.4      2023-12-06 [1] CRAN (R 4.4.0)
 httpuv        1.6.15     2024-03-26 [1] CRAN (R 4.4.0)
 jsonlite      1.8.8      2023-12-04 [1] CRAN (R 4.4.0)
 KernSmooth    2.23-24    2024-05-17 [1] CRAN (R 4.4.2)
 knitr         1.46       2024-04-06 [1] CRAN (R 4.4.0)
 labeling      0.4.3      2023-08-29 [1] CRAN (R 4.4.0)
 later         1.3.2      2023-12-06 [1] CRAN (R 4.4.0)
 lattice       0.22-6     2024-03-20 [1] CRAN (R 4.4.2)
 lifecycle     1.0.4      2023-11-07 [1] CRAN (R 4.4.0)
 magrittr      2.0.3      2022-03-30 [1] CRAN (R 4.4.0)
 markdown      1.12       2023-12-06 [1] CRAN (R 4.4.0)
 memoise       2.0.1      2021-11-26 [1] CRAN (R 4.4.0)
 mime          0.12       2021-09-28 [1] CRAN (R 4.4.0)
 miniUI        0.1.1.1    2018-05-18 [1] CRAN (R 4.4.0)
 munsell       0.5.1      2024-04-01 [1] CRAN (R 4.4.0)
 nnet          7.3-19     2023-05-03 [1] CRAN (R 4.4.2)
 patchwork   * 1.2.0      2024-01-08 [1] CRAN (R 4.4.0)
 pillar        1.9.0      2023-03-22 [1] CRAN (R 4.4.0)
 pkgbuild      1.4.4      2024-03-17 [1] CRAN (R 4.4.0)
 pkgconfig     2.0.3      2019-09-22 [1] CRAN (R 4.4.0)
 pkgload       1.3.4      2024-01-16 [1] CRAN (R 4.4.0)
 prismatic     1.1.2      2024-04-10 [1] CRAN (R 4.4.0)
 profvis       0.3.8      2023-05-02 [1] CRAN (R 4.4.0)
 promises      1.3.0      2024-04-05 [1] CRAN (R 4.4.0)
 proxy         0.4-27     2022-06-09 [1] CRAN (R 4.4.0)
 purrr       * 1.0.2      2023-08-10 [1] CRAN (R 4.4.0)
 R6            2.5.1      2021-08-19 [1] CRAN (R 4.4.0)
 ragg          1.3.2      2024-05-15 [1] CRAN (R 4.4.0)
 raster      * 3.6-26     2023-10-14 [1] CRAN (R 4.4.0)
 Rcpp          1.0.12     2024-01-09 [1] CRAN (R 4.4.0)
 readr       * 2.1.5      2024-01-10 [1] CRAN (R 4.4.0)
 remotes       2.5.0      2024-03-17 [1] CRAN (R 4.4.0)
 rlang         1.1.4      2024-06-04 [1] CRAN (R 4.4.0)
 rmarkdown     2.26       2024-03-05 [1] CRAN (R 4.4.0)
 rpart         4.1.23     2023-12-05 [1] CRAN (R 4.4.2)
 rprojroot     2.0.4      2023-11-05 [1] CRAN (R 4.4.0)
 rstudioapi    0.16.0     2024-03-24 [1] CRAN (R 4.4.0)
 scales        1.3.0      2023-11-28 [1] CRAN (R 4.4.0)
 scico       * 1.5.0      2023-08-14 [1] CRAN (R 4.4.0)
 sessioninfo   1.2.2      2021-12-06 [1] CRAN (R 4.4.0)
 sf            1.0-16     2024-03-24 [1] CRAN (R 4.4.0)
 shiny         1.8.1.1    2024-04-02 [1] CRAN (R 4.4.0)
 sp          * 2.1-4      2024-04-30 [1] CRAN (R 4.4.0)
 stringi       1.8.4      2024-05-06 [1] CRAN (R 4.4.0)
 stringr       1.5.1      2023-11-14 [1] CRAN (R 4.4.0)
 systemfonts   1.1.0      2024-05-15 [1] CRAN (R 4.4.0)
 terra         1.7-78     2024-05-22 [1] CRAN (R 4.4.0)
 textshaping   0.4.0      2024-05-24 [1] CRAN (R 4.4.0)
 tibble        3.2.1      2023-03-20 [1] CRAN (R 4.4.0)
 tidyselect    1.2.1      2024-03-11 [1] CRAN (R 4.4.0)
 tzdb          0.4.0      2023-05-12 [1] CRAN (R 4.4.0)
 units         0.8-5      2023-11-28 [1] CRAN (R 4.4.0)
 urlchecker    1.0.1      2021-11-30 [1] CRAN (R 4.4.0)
 usethis       2.2.3      2024-02-19 [1] CRAN (R 4.4.0)
 utf8          1.2.4      2023-10-22 [1] CRAN (R 4.4.0)
 vctrs         0.6.5      2023-12-01 [1] CRAN (R 4.4.0)
 withr         3.0.0      2024-01-16 [1] CRAN (R 4.4.0)
 xfun          0.49       2024-10-31 [1] CRAN (R 4.4.1)
 xml2          1.3.6      2023-12-04 [1] CRAN (R 4.4.0)
 xtable        1.8-4      2019-04-21 [1] CRAN (R 4.4.0)
 yaml          2.3.8      2023-12-11 [1] CRAN (R 4.4.0)

 [1] /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library

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